{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基于经典网络架构训练图像分类模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理部分：\n",
    "\n",
    "- 数据增强：torchvision中transforms模块自带功能，比较实用\n",
    "- 数据预处理：torchvision中transforms也帮我们实现好了，直接调用即可\n",
    "- DataLoader模块直接读取batch数据\n",
    "\n",
    "### 网络模块设置：\n",
    "\n",
    "- 加载预训练模型，torchvision中有很多经典网络架构，调用起来十分方便，并且可以用人家训练好的权重参数来继续训练，也就是所谓的迁移学习\n",
    "- 需要注意的是别人训练好的任务跟咱们的可不是完全一样，需要把最后的head层改一改，一般也就是最后的全连接层，改成咱们自己的任务\n",
    "- 训练时可以全部重头训练，也可以只训练最后咱们任务的层，因为前几层都是做特征提取的，本质任务目标是一致的\n",
    "\n",
    "### 网络模型保存与测试\n",
    "- 模型保存的时候可以带有选择性，例如在验证集中如果当前效果好则保存\n",
    "- 读取模型进行实际测试\n",
    "\n",
    "![title](1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "colab_type": "code",
    "id": "Qqk2lCKzYf-y",
    "outputId": "2354909a-d734-4dd8-e887-eb5373d9127e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\_distributor_init.py:32: UserWarning: loaded more than 1 DLL from .libs:\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.IPBC74C7KURV7CB2PKT5Z5FNR3SIBV4J.gfortran-win_amd64.dll\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.NOIJJG62EMASZI6NYURL6JBKM4EVBGM7.gfortran-win_amd64.dll\n",
      "  stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "#pip install torchvision\n",
    "from torchvision import transforms, models, datasets\n",
    "#https://pytorch.org/docs/stable/torchvision/index.html\n",
    "import imageio\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import random\n",
    "import sys\n",
    "import copy\n",
    "import json\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Wa2qCaSOd9_J"
   },
   "source": [
    "### 数据读取与预处理操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "S2lHfvKMd9_J"
   },
   "outputs": [],
   "source": [
    "data_dir = './flower_data/'\n",
    "train_dir = data_dir + '/train'\n",
    "valid_dir = data_dir + '/valid'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 制作好数据源：\n",
    "- data_transforms中指定了所有图像预处理操作\n",
    "- ImageFolder假设所有的文件按文件夹保存好，每个文件夹下面存贮同一类别的图片，文件夹的名字为分类的名字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5y6IxsmtQ0hR"
   },
   "outputs": [],
   "source": [
    "data_transforms = {\n",
    "    'train': \n",
    "        transforms.Compose([\n",
    "        transforms.Resize([96, 96]),\n",
    "        transforms.RandomRotation(45),#随机旋转，-45到45度之间随机选\n",
    "        transforms.CenterCrop(64),#从中心开始裁剪\n",
    "        transforms.RandomHorizontalFlip(p=0.5),#随机水平翻转 选择一个概率概率\n",
    "        transforms.RandomVerticalFlip(p=0.5),#随机垂直翻转\n",
    "        transforms.ColorJitter(brightness=0.2, contrast=0.1, saturation=0.1, hue=0.1),#参数1为亮度，参数2为对比度，参数3为饱和度，参数4为色相\n",
    "        transforms.RandomGrayscale(p=0.025),#概率转换成灰度率，3通道就是R=G=B\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])#均值，标准差\n",
    "    ]),\n",
    "    'valid': \n",
    "        transforms.Compose([\n",
    "        transforms.Resize([64, 64]),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ebneIHSWP74J"
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'valid']}\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True) for x in ['train', 'valid']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'valid']}\n",
    "class_names = image_datasets['train'].classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train': Dataset ImageFolder\n",
       "     Number of datapoints: 6552\n",
       "     Root location: ./flower_data/train\n",
       "     StandardTransform\n",
       " Transform: Compose(\n",
       "                Resize(size=[96, 96], interpolation=bilinear, max_size=None, antialias=None)\n",
       "                RandomRotation(degrees=[-45.0, 45.0], interpolation=nearest, expand=False, fill=0)\n",
       "                CenterCrop(size=(64, 64))\n",
       "                RandomHorizontalFlip(p=0.5)\n",
       "                RandomVerticalFlip(p=0.5)\n",
       "                ColorJitter(brightness=[0.8, 1.2], contrast=[0.9, 1.1], saturation=[0.9, 1.1], hue=[-0.1, 0.1])\n",
       "                RandomGrayscale(p=0.025)\n",
       "                ToTensor()\n",
       "                Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
       "            ), 'valid': Dataset ImageFolder\n",
       "     Number of datapoints: 818\n",
       "     Root location: ./flower_data/valid\n",
       "     StandardTransform\n",
       " Transform: Compose(\n",
       "                Resize(size=[64, 64], interpolation=bilinear, max_size=None, antialias=None)\n",
       "                ToTensor()\n",
       "                Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
       "            )}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train': <torch.utils.data.dataloader.DataLoader at 0x1e4c50b9400>,\n",
       " 'valid': <torch.utils.data.dataloader.DataLoader at 0x1e4c51ad128>}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train': 6552, 'valid': 818}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8JvBFT6qd9_M"
   },
   "source": [
    "### 读取标签对应的实际名字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "YlHjfyDRd9_N"
   },
   "outputs": [],
   "source": [
    "with open('cat_to_name.json', 'r') as f:\n",
    "    cat_to_name = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1751
    },
    "colab_type": "code",
    "id": "SiPBCbEpqX8P",
    "outputId": "0bbdec1b-65bc-4999-9e43-4b448c979cb8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': 'pink primrose',\n",
       " '10': 'globe thistle',\n",
       " '100': 'blanket flower',\n",
       " '101': 'trumpet creeper',\n",
       " '102': 'blackberry lily',\n",
       " '11': 'snapdragon',\n",
       " '12': \"colt's foot\",\n",
       " '13': 'king protea',\n",
       " '14': 'spear thistle',\n",
       " '15': 'yellow iris',\n",
       " '16': 'globe-flower',\n",
       " '17': 'purple coneflower',\n",
       " '18': 'peruvian lily',\n",
       " '19': 'balloon flower',\n",
       " '2': 'hard-leaved pocket orchid',\n",
       " '20': 'giant white arum lily',\n",
       " '21': 'fire lily',\n",
       " '22': 'pincushion flower',\n",
       " '23': 'fritillary',\n",
       " '24': 'red ginger',\n",
       " '25': 'grape hyacinth',\n",
       " '26': 'corn poppy',\n",
       " '27': 'prince of wales feathers',\n",
       " '28': 'stemless gentian',\n",
       " '29': 'artichoke',\n",
       " '3': 'canterbury bells',\n",
       " '30': 'sweet william',\n",
       " '31': 'carnation',\n",
       " '32': 'garden phlox',\n",
       " '33': 'love in the mist',\n",
       " '34': 'mexican aster',\n",
       " '35': 'alpine sea holly',\n",
       " '36': 'ruby-lipped cattleya',\n",
       " '37': 'cape flower',\n",
       " '38': 'great masterwort',\n",
       " '39': 'siam tulip',\n",
       " '4': 'sweet pea',\n",
       " '40': 'lenten rose',\n",
       " '41': 'barbeton daisy',\n",
       " '42': 'daffodil',\n",
       " '43': 'sword lily',\n",
       " '44': 'poinsettia',\n",
       " '45': 'bolero deep blue',\n",
       " '46': 'wallflower',\n",
       " '47': 'marigold',\n",
       " '48': 'buttercup',\n",
       " '49': 'oxeye daisy',\n",
       " '5': 'english marigold',\n",
       " '50': 'common dandelion',\n",
       " '51': 'petunia',\n",
       " '52': 'wild pansy',\n",
       " '53': 'primula',\n",
       " '54': 'sunflower',\n",
       " '55': 'pelargonium',\n",
       " '56': 'bishop of llandaff',\n",
       " '57': 'gaura',\n",
       " '58': 'geranium',\n",
       " '59': 'orange dahlia',\n",
       " '6': 'tiger lily',\n",
       " '60': 'pink-yellow dahlia',\n",
       " '61': 'cautleya spicata',\n",
       " '62': 'japanese anemone',\n",
       " '63': 'black-eyed susan',\n",
       " '64': 'silverbush',\n",
       " '65': 'californian poppy',\n",
       " '66': 'osteospermum',\n",
       " '67': 'spring crocus',\n",
       " '68': 'bearded iris',\n",
       " '69': 'windflower',\n",
       " '7': 'moon orchid',\n",
       " '70': 'tree poppy',\n",
       " '71': 'gazania',\n",
       " '72': 'azalea',\n",
       " '73': 'water lily',\n",
       " '74': 'rose',\n",
       " '75': 'thorn apple',\n",
       " '76': 'morning glory',\n",
       " '77': 'passion flower',\n",
       " '78': 'lotus lotus',\n",
       " '79': 'toad lily',\n",
       " '8': 'bird of paradise',\n",
       " '80': 'anthurium',\n",
       " '81': 'frangipani',\n",
       " '82': 'clematis',\n",
       " '83': 'hibiscus',\n",
       " '84': 'columbine',\n",
       " '85': 'desert-rose',\n",
       " '86': 'tree mallow',\n",
       " '87': 'magnolia',\n",
       " '88': 'cyclamen',\n",
       " '89': 'watercress',\n",
       " '9': 'monkshood',\n",
       " '90': 'canna lily',\n",
       " '91': 'hippeastrum',\n",
       " '92': 'bee balm',\n",
       " '93': 'ball moss',\n",
       " '94': 'foxglove',\n",
       " '95': 'bougainvillea',\n",
       " '96': 'camellia',\n",
       " '97': 'mallow',\n",
       " '98': 'mexican petunia',\n",
       " '99': 'bromelia'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_to_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wSBGNSA8RPfU"
   },
   "source": [
    "### 加载models中提供的模型，并且直接用训练的好权重当做初始化参数\n",
    "- 第一次执行需要下载，可能会比较慢，我会提供给大家一份下载好的，可以直接放到相应路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "OWVvodU_PfBC"
   },
   "outputs": [],
   "source": [
    "model_name = 'resnet'  #可选的比较多 ['resnet', 'alexnet', 'vgg', 'squeezenet', 'densenet', 'inception']\n",
    "#是否用人家训练好的特征来做\n",
    "feature_extract = True #都用人家特征，咱先不更新"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "pU4AlTEuiBtZ",
    "outputId": "54868c22-b059-4eda-9e4f-0911a4b372b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA is not available.  Training on CPU ...\n"
     ]
    }
   ],
   "source": [
    "# 是否用GPU训练\n",
    "train_on_gpu = torch.cuda.is_available()\n",
    "\n",
    "if not train_on_gpu:\n",
    "    print('CUDA is not available.  Training on CPU ...')\n",
    "else:\n",
    "    print('CUDA is available!  Training on GPU ...')\n",
    "    \n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型参数要不要更新\n",
    "- 有时候用人家模型，就一直用了，更不更新咱们可以自己定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "tfjrGAg5W5dE"
   },
   "outputs": [],
   "source": [
    "def set_parameter_requires_grad(model, feature_extracting):\n",
    "    if feature_extracting:\n",
    "        for param in model.parameters():\n",
    "            param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ft = models.resnet18()#18层的能快点，条件好点的也可以选152\n",
    "model_ft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 把模型输出层改成自己的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "D85BPk2GXBbj"
   },
   "outputs": [],
   "source": [
    "def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):\n",
    "    \n",
    "    model_ft = models.resnet18(pretrained=use_pretrained)\n",
    "    set_parameter_requires_grad(model_ft, feature_extract)\n",
    "    \n",
    "    num_ftrs = model_ft.fc.in_features\n",
    "    model_ft.fc = nn.Linear(num_ftrs, 102)#类别数自己根据自己任务来\n",
    "                            \n",
    "    input_size = 64#输入大小根据自己配置来\n",
    "\n",
    "    return model_ft, input_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o3PCsZshRgSG"
   },
   "source": [
    "### 设置哪些层需要训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "Nu5_c1aNU3L9",
    "outputId": "46daeef6-d017-409f-a154-8ca530765f80"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params to learn:\n",
      "\t fc.weight\n",
      "\t fc.bias\n"
     ]
    }
   ],
   "source": [
    "model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True)\n",
    "\n",
    "#GPU还是CPU计算\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "# 模型保存，名字自己起\n",
    "filename='checkpoint.pth'\n",
    "\n",
    "# 是否训练所有层\n",
    "params_to_update = model_ft.parameters()\n",
    "print(\"Params to learn:\")\n",
    "if feature_extract:\n",
    "    params_to_update = []\n",
    "    for name,param in model_ft.named_parameters():\n",
    "        if param.requires_grad == True:\n",
    "            params_to_update.append(param)\n",
    "            print(\"\\t\",name)\n",
    "else:\n",
    "    for name,param in model_ft.named_parameters():\n",
    "        if param.requires_grad == True:\n",
    "            print(\"\\t\",name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=102, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M0RxpAEhR7lL"
   },
   "source": [
    "### 优化器设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "baLfYCINRI_H"
   },
   "outputs": [],
   "source": [
    "# 优化器设置\n",
    "optimizer_ft = optim.Adam(params_to_update, lr=1e-2)#要训练啥参数，你来定\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)#学习率每7个epoch衰减成原来的1/10\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "s-PpMk90ShRz"
   },
   "source": [
    "### 训练模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EzPXJbyZXuK0"
   },
   "outputs": [],
   "source": [
    "def train_model(model, dataloaders, criterion, optimizer, num_epochs=25,filename='best.pt'):\n",
    "    #咱们要算时间的\n",
    "    since = time.time()\n",
    "    #也要记录最好的那一次\n",
    "    best_acc = 0\n",
    "    #模型也得放到你的CPU或者GPU\n",
    "    model.to(device)\n",
    "    #训练过程中打印一堆损失和指标\n",
    "    val_acc_history = []\n",
    "    train_acc_history = []\n",
    "    train_losses = []\n",
    "    valid_losses = []\n",
    "    #学习率\n",
    "    LRs = [optimizer.param_groups[0]['lr']]\n",
    "    #最好的那次模型，后续会变的，先初始化\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    #一个个epoch来遍历\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # 训练和验证\n",
    "        for phase in ['train', 'valid']:\n",
    "            if phase == 'train':\n",
    "                model.train()  # 训练\n",
    "            else:\n",
    "                model.eval()   # 验证\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # 把数据都取个遍\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)#放到你的CPU或GPU\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # 清零\n",
    "                optimizer.zero_grad()\n",
    "                # 只有训练的时候计算和更新梯度\n",
    "                outputs = model(inputs)\n",
    "                loss = criterion(outputs, labels)\n",
    "                _, preds = torch.max(outputs, 1)\n",
    "                # 训练阶段更新权重\n",
    "                if phase == 'train':\n",
    "                    loss.backward()\n",
    "                    optimizer.step()\n",
    "\n",
    "                # 计算损失\n",
    "                running_loss += loss.item() * inputs.size(0)#0表示batch那个维度\n",
    "                running_corrects += torch.sum(preds == labels.data)#预测结果最大的和真实值是否一致\n",
    "                \n",
    "            \n",
    "            \n",
    "            epoch_loss = running_loss / len(dataloaders[phase].dataset)#算平均\n",
    "            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)\n",
    "            \n",
    "            time_elapsed = time.time() - since#一个epoch我浪费了多少时间\n",
    "            print('Time elapsed {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n",
    "            \n",
    "\n",
    "            # 得到最好那次的模型\n",
    "            if phase == 'valid' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "                state = {\n",
    "                  'state_dict': model.state_dict(),#字典里key就是各层的名字，值就是训练好的权重\n",
    "                  'best_acc': best_acc,\n",
    "                  'optimizer' : optimizer.state_dict(),\n",
    "                }\n",
    "                torch.save(state, filename)\n",
    "            if phase == 'valid':\n",
    "                val_acc_history.append(epoch_acc)\n",
    "                valid_losses.append(epoch_loss)\n",
    "                #scheduler.step(epoch_loss)#学习率衰减\n",
    "            if phase == 'train':\n",
    "                train_acc_history.append(epoch_acc)\n",
    "                train_losses.append(epoch_loss)\n",
    "        \n",
    "        print('Optimizer learning rate : {:.7f}'.format(optimizer.param_groups[0]['lr']))\n",
    "        LRs.append(optimizer.param_groups[0]['lr'])\n",
    "        print()\n",
    "        scheduler.step()#学习率衰减\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "\n",
    "    # 训练完后用最好的一次当做模型最终的结果,等着一会测试\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRs "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 开始训练！\n",
    "\n",
    "- 我们现在只训练了输出层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MXbS16p_j5U0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/19\n",
      "----------\n",
      "Time elapsed 0m 39s\n",
      "train Loss: 4.0874 Acc: 0.2355\n",
      "Time elapsed 0m 43s\n",
      "valid Loss: 3.5746 Acc: 0.2531\n",
      "Optimizer learning rate : 0.0100000\n",
      "\n",
      "Epoch 1/19\n",
      "----------\n",
      "Time elapsed 1m 22s\n",
      "train Loss: 2.8185 Acc: 0.3953\n",
      "Time elapsed 1m 26s\n",
      "valid Loss: 3.5450 Acc: 0.3142\n",
      "Optimizer learning rate : 0.0100000\n",
      "\n",
      "Epoch 2/19\n",
      "----------\n",
      "Time elapsed 2m 5s\n",
      "train Loss: 2.7673 Acc: 0.4174\n",
      "Time elapsed 2m 9s\n",
      "valid Loss: 3.9110 Acc: 0.2653\n",
      "Optimizer learning rate : 0.0100000\n",
      "\n",
      "Epoch 3/19\n",
      "----------\n",
      "Time elapsed 2m 48s\n",
      "train Loss: 2.7962 Acc: 0.4255\n",
      "Time elapsed 2m 52s\n",
      "valid Loss: 3.6922 Acc: 0.3142\n",
      "Optimizer learning rate : 0.0100000\n",
      "\n",
      "Epoch 4/19\n",
      "----------\n",
      "Time elapsed 3m 32s\n",
      "train Loss: 2.7453 Acc: 0.4428\n",
      "Time elapsed 3m 36s\n",
      "valid Loss: 3.9310 Acc: 0.3044\n",
      "Optimizer learning rate : 0.0100000\n",
      "\n",
      "Epoch 5/19\n",
      "----------\n",
      "Time elapsed 4m 14s\n",
      "train Loss: 2.2935 Acc: 0.5043\n",
      "Time elapsed 4m 18s\n",
      "valid Loss: 3.3299 Acc: 0.3435\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 6/19\n",
      "----------\n",
      "Time elapsed 4m 57s\n",
      "train Loss: 2.0654 Acc: 0.5258\n",
      "Time elapsed 5m 1s\n",
      "valid Loss: 3.2608 Acc: 0.3411\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 7/19\n",
      "----------\n",
      "Time elapsed 5m 40s\n",
      "train Loss: 1.9603 Acc: 0.5369\n",
      "Time elapsed 5m 44s\n",
      "valid Loss: 3.2618 Acc: 0.3472\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 8/19\n",
      "----------\n",
      "Time elapsed 6m 23s\n",
      "train Loss: 1.9216 Acc: 0.5401\n",
      "Time elapsed 6m 27s\n",
      "valid Loss: 3.1651 Acc: 0.3386\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 9/19\n",
      "----------\n",
      "Time elapsed 7m 5s\n",
      "train Loss: 1.9203 Acc: 0.5458\n",
      "Time elapsed 7m 9s\n",
      "valid Loss: 3.0449 Acc: 0.3680\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 10/19\n",
      "----------\n",
      "Time elapsed 7m 48s\n",
      "train Loss: 1.8366 Acc: 0.5553\n",
      "Time elapsed 7m 52s\n",
      "valid Loss: 3.0722 Acc: 0.3545\n",
      "Optimizer learning rate : 0.0001000\n",
      "\n",
      "Epoch 11/19\n",
      "----------\n",
      "Time elapsed 8m 31s\n",
      "train Loss: 1.8324 Acc: 0.5546\n",
      "Time elapsed 8m 35s\n",
      "valid Loss: 3.0115 Acc: 0.3643\n",
      "Optimizer learning rate : 0.0001000\n",
      "\n",
      "Epoch 12/19\n",
      "----------\n",
      "Time elapsed 9m 13s\n",
      "train Loss: 1.8054 Acc: 0.5553\n",
      "Time elapsed 9m 17s\n",
      "valid Loss: 3.0688 Acc: 0.3619\n",
      "Optimizer learning rate : 0.0001000\n",
      "\n",
      "Epoch 13/19\n",
      "----------\n",
      "Time elapsed 9m 56s\n",
      "train Loss: 1.8436 Acc: 0.5534\n",
      "Time elapsed 10m 0s\n",
      "valid Loss: 3.0100 Acc: 0.3631\n",
      "Optimizer learning rate : 0.0001000\n",
      "\n",
      "Epoch 14/19\n",
      "----------\n",
      "Time elapsed 10m 39s\n",
      "train Loss: 1.7417 Acc: 0.5614\n",
      "Time elapsed 10m 43s\n",
      "valid Loss: 3.0129 Acc: 0.3655\n",
      "Optimizer learning rate : 0.0001000\n",
      "\n",
      "Epoch 15/19\n",
      "----------\n",
      "Time elapsed 11m 22s\n",
      "train Loss: 1.7610 Acc: 0.5672\n",
      "Time elapsed 11m 26s\n",
      "valid Loss: 3.0220 Acc: 0.3606\n",
      "Optimizer learning rate : 0.0000100\n",
      "\n",
      "Epoch 16/19\n",
      "----------\n",
      "Time elapsed 12m 6s\n",
      "train Loss: 1.7788 Acc: 0.5676\n",
      "Time elapsed 12m 10s\n",
      "valid Loss: 3.0104 Acc: 0.3557\n",
      "Optimizer learning rate : 0.0000100\n",
      "\n",
      "Epoch 17/19\n",
      "----------\n",
      "Time elapsed 12m 49s\n",
      "train Loss: 1.8033 Acc: 0.5638\n",
      "Time elapsed 12m 53s\n",
      "valid Loss: 3.0428 Acc: 0.3606\n",
      "Optimizer learning rate : 0.0000100\n",
      "\n",
      "Epoch 18/19\n",
      "----------\n",
      "Time elapsed 13m 33s\n",
      "train Loss: 1.8294 Acc: 0.5568\n",
      "Time elapsed 13m 37s\n",
      "valid Loss: 3.0307 Acc: 0.3509\n",
      "Optimizer learning rate : 0.0000100\n",
      "\n",
      "Epoch 19/19\n",
      "----------\n",
      "Time elapsed 14m 16s\n",
      "train Loss: 1.7949 Acc: 0.5612\n",
      "Time elapsed 14m 20s\n",
      "valid Loss: 3.0396 Acc: 0.3643\n",
      "Optimizer learning rate : 0.0000100\n",
      "\n",
      "Training complete in 14m 20s\n",
      "Best val Acc: 0.367971\n"
     ]
    }
   ],
   "source": [
    "model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs  = train_model(model_ft, dataloaders, criterion, optimizer_ft, num_epochs=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7rjvtqx0TGkb"
   },
   "source": [
    "### 再继续训练所有层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "cSGcACfmrIC6"
   },
   "outputs": [],
   "source": [
    "for param in model_ft.parameters():\n",
    "    param.requires_grad = True\n",
    "\n",
    "# 再继续训练所有的参数，学习率调小一点\n",
    "optimizer = optim.Adam(model_ft.parameters(), lr=1e-3)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)\n",
    "\n",
    "# 损失函数\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LFiiMNWaHXfw"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载之前训练好的权重参数\n",
    "\n",
    "checkpoint = torch.load(filename)\n",
    "best_acc = checkpoint['best_acc']\n",
    "model_ft.load_state_dict(checkpoint['state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_JCiyD-ytYRu"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/9\n",
      "----------\n",
      "Time elapsed 1m 32s\n",
      "train Loss: 2.2451 Acc: 0.4846\n",
      "Time elapsed 1m 36s\n",
      "valid Loss: 2.3190 Acc: 0.4633\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 1/9\n",
      "----------\n",
      "Time elapsed 2m 54s\n",
      "train Loss: 1.2920 Acc: 0.6505\n",
      "Time elapsed 2m 58s\n",
      "valid Loss: 2.2263 Acc: 0.4670\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 2/9\n",
      "----------\n",
      "Time elapsed 4m 15s\n",
      "train Loss: 1.1026 Acc: 0.6993\n",
      "Time elapsed 4m 19s\n",
      "valid Loss: 1.8115 Acc: 0.5452\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 3/9\n",
      "----------\n",
      "Time elapsed 5m 35s\n",
      "train Loss: 0.9062 Acc: 0.7515\n",
      "Time elapsed 5m 39s\n",
      "valid Loss: 2.0045 Acc: 0.5403\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 4/9\n",
      "----------\n",
      "Time elapsed 6m 56s\n",
      "train Loss: 0.8392 Acc: 0.7643\n",
      "Time elapsed 7m 0s\n",
      "valid Loss: 2.1381 Acc: 0.5171\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 5/9\n",
      "----------\n",
      "Time elapsed 8m 17s\n",
      "train Loss: 0.7081 Acc: 0.7953\n",
      "Time elapsed 8m 21s\n",
      "valid Loss: 2.0461 Acc: 0.5599\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 6/9\n",
      "----------\n",
      "Time elapsed 9m 38s\n",
      "train Loss: 0.6400 Acc: 0.8147\n",
      "Time elapsed 9m 42s\n",
      "valid Loss: 2.2603 Acc: 0.5452\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 7/9\n",
      "----------\n",
      "Time elapsed 10m 59s\n",
      "train Loss: 0.6406 Acc: 0.8117\n",
      "Time elapsed 11m 3s\n",
      "valid Loss: 1.4649 Acc: 0.6406\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 8/9\n",
      "----------\n",
      "Time elapsed 12m 20s\n",
      "train Loss: 0.5686 Acc: 0.8300\n",
      "Time elapsed 12m 24s\n",
      "valid Loss: 1.7538 Acc: 0.6100\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 9/9\n",
      "----------\n",
      "Time elapsed 13m 41s\n",
      "train Loss: 0.5978 Acc: 0.8245\n",
      "Time elapsed 13m 45s\n",
      "valid Loss: 1.6953 Acc: 0.6161\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Training complete in 13m 45s\n",
      "Best val Acc: 0.640587\n"
     ]
    }
   ],
   "source": [
    "model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs  = train_model(model_ft, dataloaders, criterion, optimizer, num_epochs=10,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EbetBY4bd9_U"
   },
   "source": [
    "### 加载训练好的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "0IXiW9_2d9_U",
    "outputId": "2f21f369-df6a-433f-812a-1bbb87d30fa6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True)\n",
    "\n",
    "# GPU模式\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "# 保存文件的名字\n",
    "filename='best.pt'\n",
    "\n",
    "# 加载模型\n",
    "checkpoint = torch.load(filename)\n",
    "best_acc = checkpoint['best_acc']\n",
    "model_ft.load_state_dict(checkpoint['state_dict'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PXCZPK70d9_W"
   },
   "source": [
    "### 测试数据预处理\n",
    "\n",
    "- 测试数据处理方法需要跟训练时一直才可以 \n",
    "- crop操作的目的是保证输入的大小是一致的\n",
    "- 标准化操作也是必须的，用跟训练数据相同的mean和std,但是需要注意一点训练数据是在0-1上进行标准化，所以测试数据也需要先归一化\n",
    "- 最后一点，PyTorch中颜色通道是第一个维度，跟很多工具包都不一样，需要转换\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 得到一个batch的测试数据\n",
    "dataiter = iter(dataloaders['valid'])\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "model_ft.eval()\n",
    "\n",
    "if train_on_gpu:\n",
    "    output = model_ft(images.cuda())\n",
    "else:\n",
    "    output = model_ft(images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "output表示对一个batch中每一个数据得到其属于各个类别的可能性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([128, 102])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 得到概率最大的那个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 34,  49,  43,  54,  20,  14,  49,  43,  50,  20,  19, 100,  78,\n",
       "        96,  96,  62,  62,  63,  32,  38,  82,  43,  88,  73,   6,  51,\n",
       "        43,  89,  55,  75,  55,  11,  46,  82,  48,  82,  20, 100,  48,\n",
       "        20,  24,  49,  76,  93,  49,  46,  90,  75,  89,  75,  76,  99,\n",
       "        56,  48,  77,  66,  60,  72,  89,  97,  76,  73,  17,  48,  39,\n",
       "        31,  19,  74,  61,  46,  93,  80,  27,  11,  91,  18,  23,  47,\n",
       "        29,  54,  18,  93,   1,  50,  79,  96,  39,  53,  63,  60,  49,\n",
       "        23,  23,  52,  99,  89,   3,  50,  64,  15,  19,  60,  19,  75,\n",
       "        50,  78,  82,  18,  75,  18,  82,  53,   3,  52,  60,  38,  62,\n",
       "        47,  21,  59,  81,  48,  89,  64,  60,  55, 100,  60], dtype=int64)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, preds_tensor = torch.max(output, 1)\n",
    "\n",
    "preds = np.squeeze(preds_tensor.numpy()) if not train_on_gpu else np.squeeze(preds_tensor.cpu().numpy())\n",
    "preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 展示预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def im_convert(tensor):\n",
    "    \"\"\" 展示数据\"\"\"\n",
    "    \n",
    "    image = tensor.to(\"cpu\").clone().detach()\n",
    "    image = image.numpy().squeeze()\n",
    "    image = image.transpose(1,2,0)\n",
    "    image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))\n",
    "    image = image.clip(0, 1)\n",
    "\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nvns0dPkaDDEqeCboa3Y3YOoPXvmyf60U62AXqGwDw+Vx83q9uTe9MfwxJE37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\n3G4zPp9JkDaX9XyelfooWbamn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/7+si55e86tyT142pendATf2JejK+WlJuexKtfFfJI7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\n4HdC68ysLu2kuWI747Y/IIOer6s7kvNnW+6jMzPF9OCE3CMk1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GhE/GggOW20XusU53TY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5GGbNbN3MNkOeiPbb7Wa9eXuujdfxEvMdhbe7X3/B8oelZyz/C3S04tXY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/1z4nTRnGvxJCMMya+PbQRj40PT8J1hDxvKF9U/rDLkhbjYBuq2IwgClxj/Qut/5Jxzcc/P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\nA7Q9filXZ1lDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(20, 20))\n",
    "columns =4\n",
    "rows = 2\n",
    "\n",
    "for idx in range (columns*rows):\n",
    "    ax = fig.add_subplot(rows, columns, idx+1, xticks=[], yticks=[])\n",
    "    plt.imshow(im_convert(images[idx]))\n",
    "    ax.set_title(\"{} ({})\".format(cat_to_name[str(preds[idx])], cat_to_name[str(labels[idx].item())]),\n",
    "                 color=(\"green\" if cat_to_name[str(preds[idx])]==cat_to_name[str(labels[idx].item())] else \"red\"))\n",
    "plt.show()"
   ]
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